A pragmatic approach to significant environment information collection to support object reuse: Paper

Abstract

When aiming to ensure the long-term usage of digital objects, it is important to carefully select what information to keep, considering also what lives outside of them. In the PERICLES project we start by analysing how such information has been described in related work, considering common definitions of metadata, context, significant properties and environment, and we come to the conclusion that we need to consider the broadest set of information, which we term environment information. Building on previous definitions, we introduce the concept of Significant Environment Information (SE1) that takes into account the dependencies of the digital object on external information for specific purposes and significance weights that express the importance of such dependencies for the specific purpose. From there we expand the definition in time considering the importance of collecting SEI during any phase of the digital object lifecycle, following the sheer curation perspective. Examples of SEI are illustrated in the very diverse use cases considered in the project, that include diverse data types from the Art domain and data from space observations in the Science domain. Finally we introduce our PERICLES Extraction Tool, that we developed to capture SEI, and present methods to extract SEI with experimental results supporting the approach. The PET tool automates the novel techniques we describe, supports sheer curation, as a continuous transparent collection process that otherwise the user (e.g. scientist, artist in our use cases) would have to find time to perform manually.

Details

Creators
Fabio Corubolo; Anna Eggers; Adil Hasan; Mark Hedges; Simon Waddington; Jens Ludwig
Institutions
Date
Keywords
digital preservation; significant properties; significant environment information; environment information; dependency graph; sheer curation; significance weight; dependency extraction
Publication Type
paper
License
CC BY-NC-SA 3.0 AT
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